A novel method of image Gaussian noise filtering based on series connected PCNN model

Jinping He; Kun Gao; Guoqiang Ni

doi:10.1117/12.805707

27 January 2009 A novel method of image Gaussian noise filtering based on series connected PCNN model

Jinping He, Kun Gao, Guoqiang Ni

Proceedings Volume 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments; 71561I (2009) https://doi.org/10.1117/12.805707
Event: International Conference of Optical Instrument and Technology, 2008, Beijing, China

Abstract

In this paper, a new image de-noising algorithm based on series connected pulse-coupled neural networks (PCNN) model is presented. Traditional PCNN is a single layer model, which is suitable for real-time image processing. In this article, a new improved PCNN model called the 'series connected PCNN' is proposed, and the traditional PCNN model has been rationally simplified. The simplified 'series connected PCNN' model has less iterative times, and it's more sensitive to image edges than the traditional model. The experimental results show that the new algorithm is very effective and provides better performance in protecting image edges compared with the median filter.

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Jinping He, Kun Gao, and Guoqiang Ni "A novel method of image Gaussian noise filtering based on series connected PCNN model", Proc. SPIE 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments, 71561I (27 January 2009); https://doi.org/10.1117/12.805707

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KEYWORDS

Image filtering

Neurons

Digital filtering

Binary data

Image processing

Neural networks

Gaussian filters

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